Create app.py

app.py

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+# @title Think Paraguayo
+
+import os
+import random
+import time
+
+os.system("pip install gradio, llama_index, ragatouille, llama-cpp-python")
+os.system("git clone https://github.com/EnPaiva93/think-paraguayo-space-aux.git")
+os.system("wget https://huggingface.co/thinkPy/gua-a_ft-v0.1_mistral-7b_GGUF/resolve/main/gua-a_mistral-7b_q4_K_M.gguf -O model.gguf")
+
+from llama_cpp import Llama
+import gradio as gr
+from ragatouille import RAGPretrainedModel
+from llama_index.core import Document, SimpleDirectoryReader
+from llama_index.core.node_parser import SentenceSplitter
+
+max_seq_length = 512 # Choose any! We auto support RoPE Scaling internally!
+
+prompt = """Responde a preguntas de forma clara, amable, concisa y solamente en el lenguaje español, sobre el libro Ñande Ypykuéra.
+Contexto
+-------------------------
+{}
+-------------------------
+### Pregunta:
+{}
+### Respuesta:
+{}"""
+
+# Initialize the LLM
+llm = Llama(model_path="model.gguf",
+            n_ctx=512,
+            n_threads=2)
+
+DOC_PATH = "/app/think-paraguayo-space-aux/index" 
+
+print(os.getcwd())
+
+documents = SimpleDirectoryReader(input_files=["/app/think-paraguayo-space-aux/libro.txt"]).load_data()
+
+parser = SentenceSplitter(chunk_size=128, chunk_overlap=64)
+nodes = parser.get_nodes_from_documents(
+    documents, show_progress=False
+)
+list_nodes = [node.text for node in nodes]
+
+print(os.getcwd())
+
+if os.path.exists(DOC_PATH):
+  RAG = RAGPretrainedModel.from_index(DOC_PATH)
+else:
+  RAG = RAGPretrainedModel.from_pretrained("AdrienB134/ColBERTv2.0-spanish-mmarcoES")
+  my_documents = list_nodes
+  index_path = RAG.index(index_name=DOC_PATH, max_document_length= 100, collection=my_documents)
+
+# def convert_list_to_dict(lst):
+#   res_dct = {i: lst[i] for i in range(len(lst))}
+#   return res_dct
+
+def reformat_rag(results_rag):
+  if results_rag is not None:
+    return [result["content"] for result in results_rag]
+  else:
+    return [""]
+
+# def response(query: str = "Quien es gua'a?", context: str = ""):
+#   # print(base_prompt.format(query,""))
+#   inputs = tokenizer([base_prompt.format(query,"")], return_tensors = "pt").to("cuda")
+#   outputs = model.generate(**inputs, max_new_tokens = 128, temperature = 0.1, repetition_penalty=1.15, pad_token_id=tokenizer.eos_token_id)
+#   return tokenizer.batch_decode(outputs[0][inputs["input_ids"].shape[1]:].unsqueeze(0), skip_special_tokens=True)[0]
+
+
+def chat_stream_completion(message, history):
+
+    context = reformat_rag(RAG.search(message, k=1))
+    context = " \n ".join(context)
+
+    full_prompt = prompt.format(context,message,"")
+    print(full_prompt)
+
+    response = llm.create_completion(
+        prompt=full_prompt,
+        temperature=0.01,
+        max_tokens=256,
+        stream=True
+    )
+
+    # print(response)
+    
+    message_repl = ""
+    for chunk in response:
+      if len(chunk['choices'][0]["text"]) != 0:
+        # print(chunk)
+        message_repl = message_repl + chunk['choices'][0]["text"]
+      yield message_repl
+
+
+# def answer_question(pipeline, character, question):
+# def answer_question(question):
+#   # context = reformat_rag(RAG.search(question, k=2))
+#   # context = " \n ".join(context)
+#   yield chat_stream_completion(question, None)
+
+# def answer_question(question):
+#   context = reformat_rag(RAG.search(question, k=2))
+#   context = " \n ".join(context)
+#   return response(question, "")
+
+# def random_element():
+#         return random.choice(list_nodes)
+
+# clear_output()
+print("Importación Completada.. OK")
+
+css = """
+    h1 {
+        font-size: 32px;
+        text-align: center;
+    }
+    h2 {
+        text-align: center;
+    }
+    img {
+        height: 750px;  /* Reducing the image height */
+    }
+    """
+
+def main():
+    with gr.Blocks(css=css) as demo:
+        gr.Markdown("# Think Paraguayo")
+        gr.Markdown("## Conoce la cultura guaraní!!")
+
+        with gr.Row(variant='panel'):
+            with gr.Column(scale=1):
+                gr.Image(value="/app/think-paraguayo-space-aux/think_paraguayo.jpeg", type="filepath", label="Imagen Estática")
+
+            with gr.Column(scale=1):
+                # with gr.Row():
+                #     button = gr.Button("Cuentame ...")
+                # with gr.Row():
+
+                #     textbox = gr.Textbox(label="", interactive=False, value=random_element())
+                #     button.click(fn=random_element, inputs=[], outputs=textbox)
+
+                # with gr.Row():
+                chatbot = gr.ChatInterface(
+                    fn=chat_stream_completion,
+                    retry_btn = None,
+                    stop_btn = None,
+                    undo_btn = None
+                ).queue()
+                # with gr.Row():
+                #     msg = gr.Textbox()
+                # with gr.Row():
+                #     clear = gr.ClearButton([msg, chatbot])
+
+                # def respond(message, chat_history):
+                #     bot_message = answer_question(message)
+                #     print(bot_message)
+                #     chat_history.append((message, bot_message))
+                #     time.sleep(2)
+                #     return "", chat_history
+
+                # msg.submit(chat_stream_completion, [msg, chatbot], [msg, chatbot])
+
+
+    demo.launch(share=True, inline= False, debug=True)
+
+main()